General Content Perception and Selection System.

ABSTRACT

This invention is directed toward a system which can “step into the shoes” of a user and learn the perspective of that user, regarding photographs or other content, to the point where the system can learn, using criteria it has developed through its interaction with the user, to select photographs it predicts the user will find meaningful from large sets of photographs. The “meaningfulness” of various content from a multitude of users is a constantly improving system made up of four basic elements: a General Content Perspective, an Individual Content Perspective, a Natural Language Generation and Content Presentation, and a Hypersphere element.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority back to U.S. Provisional No. 62/745,428with a filing date of 14 Oct. 2018, the contents of which areincorporate by reference into this application.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was not federally sponsored.

INVENTORS

Troy DeBraal, a citizen of USA and resident of San Diego, Calif.,Nethika Sahani Suraweera, PhD, a permanent resident of the USA andresident of Morrison, Colo., and, Justin Williams, a citizen of the USAand resident of Littleton, Colo.

ATTORNEY DOCKET NO.

DeBraal-UP-1

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates to the general field of systems for learning theuser perspective regarding the meaningfulness of various content from amultitude of people, and more specifically, to a system that analyzesthe individual user perspective of images, such as, in a preferredembodiment, the perspective of a user regarding photographic imagescreated by the users and constantly refines its understanding of theuser perspective to provide users with an ever-improving selectionprocess by which a user is advised on which photographs the user islikely to find meaningful and desire to preserve, use and/or enhance tofurther increase their meaningfulness. The invention has severalpossible criteria, including the motivations of the users, the personalpreferences of users, the personal aesthetic of users, the criteriabeing used in photography contests, the criteria used when selectingphotos for commercial applications, etc.

This invention uses the terms “learning the user perspective” and“meaningfulness” because those terms best map to what the system isdoing, that is, it is learning what is meaningful from the user'sperspective which spans liking or preference, the deeper themes ofmotivation which can affect liking and preference but are distinct andinclude subjective user views on content/image qualities such as contentthat is needed in order for an image to be considered meaningful likethe presence of people, which may be a preference but can also be anexpression of a foundational motivation, e.g. social cooperation, thatis not a conscious preference of the user but rather a compelling forcelinked to the underlying, unconscious emotional need to foster and seekbelonging. Personalizing content and/or curating images on behalf of aperson is a difficult problem to tackle because the cognitive processesinvolved are too complex to faithfully reproduce and therefore theoutputs of these processes cannot be reproduced precisely with absoluteconfidence. However, the invention can model the core parts of the how auser perceives content by combining non-similar models in a system andmethod that allows the models to affect each other in a way thatimitates layered learning in the brain, learns from the complexinterplay of personally meaningful preferences of a human being andproduces similar outputs to the more complex human cognitive processes.

For instance, the invention imitates how new information can beintroduced that alters the perspective of the individual on isolateddimensions. When this occurs, the analysis of the sameintellectual/visual curation problem, once and then once again afterconsidering the new info, would yield different results. The inventionalso allows new information to alter the models of individualperspectives which changes the prediction outcomes of future analysis.In this way, the invention is more flexible to the ever-changingperspectives, preferences and behaviors of an invention user in a waythat models visual perspective understanding to create more precise andmore accurate results over prior art. Prior art, also recognizing theselimitations and hurdles, have chosen to approach the problem using imageto image comparison where the objective qualities of an image aremeasured or assumed and compared to other images or preferred values, sothe images can be ranked.

Prior art is flawed in many ways but primarily it is flawed in itsattempt to solve the content personalization/image curation problem byabstracting the qualities of the content/image instead of, as in theinvention, abstracting the way a person perceives content and images andtransforming this understanding into a system capable ofpersonalizing/curating content and images. Furthermore, the inventionrecognizes and solves for the problem of system user perspectivefluidity, i.e. the human tendency to make different decisions regardingsimilar content based on context and small situational details. Priorart doesn't account for this fact of human behavior and thereforeproduces poor quality results applying the same personalization/contentselection logic ubiquitously across various contextually differentsituations.

To add clarity to the invention abstraction process, prior art hasapproached the problem as one that can be solved by trying tomodel(abstract) the qualities of content in an objective way and thencomparing those results with the model/abstract of another piece ofcontent or an idealized model of the content qualities or both. Theinvention improves on prior art by realizing that prior art was startingand focusing the abstraction/modelling process on the wrong component ofthe problem space. The invention focuses on improving the system'sunderstanding of the user perspective using the user's own content as aguide at the beginning of the process by modeling what we believe arethe best and most predictive subjective visual perspectives of anindividual before attempting to evaluate the content itself.

Then, we move onto the processes where prior art begins, i.e.modelling/abstracting the qualities of content. Because we have thecritical human perspective modelling step in our invention prior tomodeling/abstracting the qualities of content in an objective way, weare able to significantly improve the accuracy of those objectivecontent qualities abstractions/models/predictions in reference to howthe invention end user would see the same analysis problem. Also, theinvention can then directly produce various predictions about subjectivecontent qualities that prior art must explicitly ask about to obtain anydata for analysis. Our invention, uniquely, generates its own accurateprediction data about subjective perspectives during content analysisand selection.

In short, the novelty and advantage of our invention is that we realizedabstracting the visual perspective of an individual, instead of theaesthetic qualities of a piece of content, allows the system to producemore varied, more accurate and precise content meaningfulnessselections. We transformed the outcomes of the system by transformingthe process into one that starts and projects from a more advantageousposition in the problem space.

The invention solves for system user perspective fluidity by recognizingthat context is a catalyst for triggering different motivationalresponses from system users. In turn, based on the mix of coremotivations, different behavioral patterns and decision biases emerge insystem users that are aligned to the underlying unconscious motivationalfactors. Therefore, when a user is considering a piece of content/imagehow they perceive the image details/qualities is directly affected bythe unconscious motivationally driven behavior patterns and biases.

That means, what a user consciously likes, wants, prefers or findsmeaningful changes based on context, motivation and then the details ofthe content/image in question in addition to the conscious preferencesof the system user. The invention includes this sequence of cause andeffect in its analysis flow. This understanding of the core elementsthat drive personalization and content curation decision making allowthe invention to drastically improve on the outputs of prior art bymimicking not the whole human cognitive process but rather just the mostimportant parts of the cognitive processes behind contentpersonalization and the flow of dependent events, inputs and outputs.

To further illustrate how the invention transforms data about visualmedia and user perspectives into an autonomous selection system bymimicking only the most important cognitive elements behind contentpersonalization, it is important to note that the invention learns toidentify what motivational, emotional, subject matter and visualqualities an individual finds meaningful, then expands its baselineunderstanding of how those meaningful qualities are visually expressedin content on an individual quality level by introducing the individualuser's subjective perspective on those meaningful visual expressions.The invention then uses the combination of those understandings toanalyze content for meaningful qualities and predict the strength ofthose qualities. In this way, the system can assume the perspective ofthe user using the most influential building blocks of the user's visualperspective on content personalization to guide the predictiveperformance of the selection invention. This method of the invention ismore flexible to serve more individuals accurately than prior art andmore efficient by reducing the solution variables to the mostinfluential and predictive perspective dimensions.

Additionally, a preferred embodiment of the system is Machine Learningbased and learns partially through non-directed pattern recognition.This non-biased observation of system user behavior patterns gives thesystem the ability, when coupled with the plurality of data dimensionswe've specifically chosen to model, to recognize meaningfulcontent/images on behalf of a user where the factors involved inchoosing the right piece of content might not be recognizable or obviousto human system designers.

In some cases, using a special time function that mimics human memoryprogression, the system can identify a small set of meaningful images,out of a large set, that might not be preferred in the moment by a userbut will be significantly meaningful to them at some point in thefuture. This ability to consider and act on the effect time has on theperception of meaningfulness is a key benefit that is not supported inprior art.

The system, which has several possible embodiments in a number ofdifferent fields, is capable of learning an individual's perspective ona variety of content and using that understanding to curate content forthe user in proxy. Possible uses of the system include identifying themost meaningful images from a large set, curating and making archive,preservation, inclusion/exclusion recommendations, analyzing photos fromthe user's aesthetic perspective and making enhancement/improvement tipsand matching users with content unknown to them across a wide variety ofsubjective influenced criteria and objective standards/best practices.The system can create these benefits for an individual system user orcompany concerning personal photography, personal video, personal music,general photography, general video, general music, brand aligned images,contest photography, contest video, consumer advertising images,educational images and behavior/mood image-based therapies. The systemis also able to learn the collective perspective of a group of peopleand perform the same beneficial tasks to produce the same benefits. Forexample, an advertising agency might collectively use the system underone account and the system would learn the combined collectivesubjective perspective of the various users using the same account tohelp curate advertising images and find ones that are meaningful oraligned to the collective subjective view of the agency instead of anindividual person.

BRIEF DESCRIPTION OF INVENTION

For sake of simplicity, a basic version of the invention will bedescribed. In this case, a person who likes to take photographs will usethe system to try to select his/her “most meaningful” photographs basedon his/her own input into the system. Although one of the preferredembodiments of the invention is used for purposes of illustration, thisin no way intends to limit the breadth of this patent application or thevarious possible embodiments of the invention in the various fields inwhich it can be used.

In this preferred embodiment, a user of the invention interfaces withthe system so that the system can learn about the user perspectiveregarding content/images/photography through a variety of processesincluding a) explicit learning where the system is asking the user aboutobjective and subjective qualities to learn their perspective directlyfrom the user and discern what is motivating the user perspectives,behaviors and preferences by matching them to archetype motivationalpersonas with motivationally matched core behaviors, preferences andbiases designed into the baseline system; and b) implicit learning wherethe system analyzes the user content, behavior(and changes in both) forpatterns to infer learnings about the user's perspective on objectiveand subjective qualities, their individual motivations and preferencesacross a variety of dimensions. This second set of learnings allow thearchetype motivational persona driving the system AI to be transformedinto an AI that is unique to the individual system user.

To elaborate on this transformational moment that allows the inventionto produce outputs not possible with prior art, the subjective learningsin the individual system user AI act as a lens or how a color overlaywould change the perception of a scene by a human viewer. The subjectivelearnings in the individual system user AI directly influence the wayall future content is perceived by the invention. In the case of theinvention, the lens like distortion of the scene/content is intentionaland correctional. The distortion or shaping of the system perceptionrepresents the particular visual perspectives, behaviors, biases andcontent preferences of the individual user. The way in which theselearnings are collected and recorded is in and of itself novel in theway that it represents the objectively subjective and objective visualperspectives of an individual user. However, the method of applyingthese subjective learnings in the invention creates unique benefits forinvention users including improved accuracy and precision of contentmeaningfulness selections.

To accomplish this image analysis task from the user's perspective in aprecise way, the system uses a many dimension hyperspace volumetricanalysis that makes predictions of the user's perspective, first acrossuniversal/objective dimensions, then, second, across subjectivedimensions that are normalized into a value that can be plotted in ahyperspace volume.

This normalized data is then plotted relative to an idealized, personalcenter, the nexus of meaningfulness, one for each user of the invention,which represents that user's perspective on the ideal state ofmeaningfulness for that type of content, e.g. photos. The system thencreates a hypersphere boundary at a distance from the idealized personalcenter. The hyperspace locations inside the hypersphere volume representlocations that are near enough to the idealized, personal center thatthey are recognized as having a high probability of being highlymeaningful to the individual. The relative distance from the center ofthe hypersphere is therefore how meaningful the system predicts the userwill perceive the given content.

The system recognizes that the distance in time fromcontent/image/photograph creation affects how meaningful the user of theinvention perceives the content/image/photography to be. To reflect thisunderstanding in the system operations, the system is alwaysrecalculating the location of the ideal personalized center in referenceto real world time. This means, unlike existing systems, the inventionnever analyses an image the same way twice because the location of theidealized, personal center is always moving according the time functionand its location is the basis for analysis. Further, the effect time hason the perception of meaningfulness in a system user is not linear. Thesystem uses a modified inverted bell curve to reflect how themeaningfulness of an image increases, wanes and then increases againover time.

Next, a user of the invention provides a variety of photographs, into a“personalization funnel”, which relies on implicit learning to findcertain pattern, quality and quality strength trends in the visualcontent such as photographs that the person finds meaningful. The systemanalyzes the photographs creating predictions of the user's perspectiveregarding the photograph. In one implementation, the system analyzes thecontent first across subjective dimensions, then, second, staticdimensions, then, third, machine learning dimensions, and finally,algorithmic dimensions to produce normalized data describing what thesystem predicts the user's perspective would be across all thedimensions so that the data can also be plotted into the hyperspacevolume for analysis. Once this plotting in hyperspace happens for a setof photographs that have been analyzed by the system, all the individualasset locations can be compared to the location of ideal personalizedcenter and the location of the boundary of the hypersphere.

The individualized asset is then filtered through a Magnitude filter,which creates a vector from the plot of the asset to the idealizedcenter and measures the magnitude of the vector to determine if it fallswithin the hypersphere. This generates data that explains dimension andmagnitude relationships through natural language generation. The step ofgenerating the dimension and magnitude relationship also received inputfrom the personalization funnel. The results from this data managementand analytical process are passed on to the user in a context whichallows the user to interact with the data and provide feedback via inputvia the keyboard and mouse, through interactions and buttons that can beactivated by the user to register feedback on individual and groups ofphotos.

In a preferred embodiment, users have the ability to comment on thesystem's natural language feedback to the user concerning the decisionlogic, as well as providing feedback, which provide the ability forusers to remove photos from the top photos or most meaningful photos andto make choices in the image enhancement module where the systemsuggests ways to enhance photos to make them better photos. Thisfeedback happens through direct user input via the keyboard and mouse,through touchscreen interactions and/or buttons that can be activated bythe user through various mechanisms to register feedback on individualand groups of photos.

As noted, in a separate part of the invention that is preferred but notrequired for the system to work and produce benefits, a user of theinvention provides his/her pictures to an explicit learning component ofthe system, which provides continuous learning and improvement to theselection assessment system. These results can be transmitted tocontinuously train a universal model, a persona model, and an individualmodel. Each of these models can be further improved through the input ofthe context of each component. The initial invention relied on the userto choose arbitrary photos or images for submission during explicitlearning, we've now modified the photo submission explicit learning stepto include a prompt to a self-identified photo of themselves from theirphoto library, selfie or other, that further improves the system'sability to accurately predict subject matter relationships andimportance.

Time has a special function in the system and is constantly altering theuser perspective models based on a modified inverted bell curve wherevery recent images/events identified in images are judged as probablymore meaningful to a person than images/events slightly farther away intime with a reversal as images/events identified in images get fartheraway in time where, over a long period of time, images/events identifiedin images are judged as probably even more meaningful than recent eventsonce they reach a specified threshold of distance in the past. Theseimprovements are constantly subject to improvement as the user'sfeedback is transmitted back to the system in the form of an explicitand continuous feedback loop.

The basic goal of this system is to help a person identify and use themost meaningful photos out of a large set. It accomplishes this task byusing computer software and continuous feedback to allow the computer tolearn an individual's subjective perspective regarding photos, then usethat understanding of the individual's subjective perspective to suggestto the user photos he or she may find meaningful and worthy ofpreservation, use or enhancement. The possible uses of the inventionrange from helping a hobbyist photographer to select from a weeklongtrip's worth of photos a few that the system predicts will be the user'sfavorites, to helping professional photographers enter photos most liketo win photography contests based on the criteria the contest has usedin the past to pick winning photographs; to automating the process ofmobile phone photo library curation and selection for processes likeprinting, sharing and archiving.

The prior art has several examples of attempts to resolve this problem.For example, Canadian Patent Application 2 626 323 to Hale, et. al,teaches a method of automatically tagging photo images. While tagging ofphoto images is helpful, it does not address the benefit of makingautomated selections for users of the invention based on the systemlearning an individual's preferences. U.S. Pat. No. 7,924,473 to FujiFilm uses an image evaluation process to judge the printing status of aparticular photograph, with an “evaluation value” being assigned to thephotograph. This system does not provide the “system learning” by whichthe systems steps into the shoes of the user and “selects” for the userphotographs the user is likely to find meaningful.

U.S. Pat. No. 8,531,551 to Huberman, et. al. provides a system andmethod for image sharing. This patent, however, does not provide thecontinuous-loop feedback that allows the current invention to improveitself over time. It also assumes that photographs within a “set” ofphotographs are to be transmitted and analyzed. Another U.S. Pat. No.8,634,646 to Herraiz, et. al. teaches yet another method of selectingand recommending photograph though attaching various tags and scores tophotographs. This patent fails to improve upon itself, or to actuallytake on the role of actively selecting photographs that the systembelieves the user will like. U.S. Pat. No. 8,873,851 to Federovskaya,et. al. lays out a system for selecting “high-interest-level images”.This system requires the use of a digital camera that captures images ofvarious people viewing an image, and then assessing an “interest level”to certain images for certain people. While this system may be able toaccurately assess a person's interest in a particular photograph, itdoes not provide the feedback that the current invention provides toconstantly improve its accuracy to the point with the current system canactually go into a batch of photographs and select optimum ones for theuser of the invention.

IBM also got into the photo selection business with U.S. Pat. No.8,925,060 to Chakra, et. al. This method uses a cloud computing systemto identify a certain criterion and determine whether that criteria isauthorized for transmission to storage in the cloud. This patent doesnot provide a solution related to photograph preference and machinelearning. Another patent dealing with photograph selection is U.S. Pat.No. 9,268,792 to Morrison, et. al. This patent uses weighted criteria toselect an image from a group of images. As with the rest of the priorart, while this patent may assist in selecting an image that aparticular user may find attractive, it does not use machine learning toselect photos as though it was the person, and does not have thefeedback loop function to constantly improve itself.

A final piece of prior art is US Publication No. 2015/0213110 toKazunaori Araki. This publication discusses the use of software tocalculate scores, including scores associated with pictures. The methodtaught by the Kazunaori publication does not allow the user to benefitfrom the system getting to know him/her well enough to suggestphotographs to him/her, but rather just abstracts measurable quantitiesof an image and user behavior.

Thus there has existed a long-felt need for a system that can learn whata user finds meaningful to the point where the system can accuratelypredict which media a user will find meaningful, or, in this preferredembodiment of the invention, the system will predict which photographs auser will find meaningful from a set of photographs entered into thesystem.

The current invention provides just such a solution by having a systemwhich learns an individual's perspective on photographs, through avariety of inquiries and observations, for example through question andimage surveys. Machine learning is then used in a number of ways toenhance the ability of the system to anticipate a user's perspective andwhat they find meaningful. The current invention creates benefits notseen in prior art including a) producing higher quality contentpersonalization and image curation results, this is possible in part bypredicting the subjective user perspective on content as opposed totrying to measure or infer the objective qualities of an image and thencomparing those qualities to other images to rank them and in partbecause the invention abstracts the cognitive process of imageperception instead of abstracting image qualities.

By moving the abstraction from the content/image level to the cognitiveprocess layer of the problem space, superior results are achievablebecause nuances of human decision making, e.g. the sequence of eventswhere context affects motivation, can be built into the processimproving the fidelity and effectiveness of the system b) producingbenefits in a broader set of applications and situations, this ispossible in part because the system analysis has motivational biasbaselines that change based on context detected in the content beinganalyzed and also because the criteria the invention uses can be changedto a bespoke set of criteria that is more applicable to a certain typeof content.

That is, prior art systems are focused on silos of content because theyfocus on abstracting qualities of the content and those abstractions arenot applicable in disparate situations whereas the current inventiondoes not have this limitation as it can be applied to many differentcontent personalization and curation tasks c) producing more preciseresults in situations where there are many individual assets or piecesof content to be analyzed because the system self improves and is betterat meaningfulness analysis each time and because the hypersphere allowssubtle differences in motivationally significant dimension values toimpact overall system results and push images into the boundary ofmeaningfulness whereas the difference might go unnoticed in prior art d)producing more concise results through the removal of duplicate and nearduplicate images from final selection sets e) the learnings from oneuser can automatically be conveyed to other users of the same type toimprove the overall system in addition to the individual systemamplifying the impact of continuous learning to produce better andbetter results overtime.

The system has continuous learning and improvement from multiplefeedback sub-systems, allowing the system to constantly improve itspredictive abilities with each user. In a clear improvement on priorart, the invention feedback sub-systems not only improve the predictiveabilities of the system for an individual user, but also many generallearnings about people, their behavior and preferences in reference totheir motivations are fed back into the archetype AI personas improvingthe predictive abilities for all individuals as the system is used bymore people.

A “personalization funnel” takes in all the content and predictivecomponents, including a universal model, a persona model and anindividual model, and, through implicit and explicit learning, generatesan ever-improving predictive model. From the personalization funnel, thedata goes through a de-duplication process by which duplicate andnear-duplicate images are identified, with only the best image(s), withlesser quality near duplicates removed, selected for presentation to theuser.

The original invention relied on a de-duplication system focused onremoving duplicates and identifying near duplicates to find theaesthetically best photo of the bunch, the invention has been modifiedto allow the personalization funnel to more directly impact similarityanalysis and amplify the personalization funnel effect. The subjectiveperspective effect of the personalization funnel is of criticalimportance when detecting the most meaningful photo/piece of contentfrom a set of near duplicates because the capture of multiple pieces ofcontent that is highly similar, especially in close succession, is oftenan indicator of subjective preference for the features of that media, souser intent of subjective preference can be inferred and used to applypositive weight to the evaluation of those dimensions of user content inthat and future analysis of other content.

Likewise, media content that is similar but not duplicate or part of anear-duplicate series implies subjective perspective of the user and canbe used by the invention to adjust system predictive parameters, such asweight adjustments for rewards and penalties to the individualdimensions which contain such similarities across content during currentand future analysis.

The personalization funnel can influence each, individual dimensionevaluated thereafter, from other machine-learning dimensions whichinclude, for example, the de-duplication process, to algorithmicdimensions which take as an input those personalization tensors. It eveninfluences the evaluation of static dimensions through the followingprocess of dimension normalization. This uses basic lineartransformations such as stretch and scale, as well as flattens andconnects axis gaps for the more complex dimensions so that alldimensions are comparable to each other for plotting assets across alldimensions later. The personalization influence of this normalizationcan be taught with its own weights and biases through implicit andexplicit training of the system, allowing for intricate, nuancedinteractions across dimensions for personal meaningfulness.

The de-duplication function serves to limit the selection of duplicateand near-duplicate images, so that the results are more concise and ofhigher value to the system user. For example, in the preferredembodiment illustrated here, a viewer will benefit more from his/her“best/most meaningful” pictures of a particular object from a series—asassessed by the system—rather than being presented with a number ofidentical or nearly identical images. In other embodiments of theinvention, the de-duplication and series identification functions arecoupled with additional layers of image identification to remove memesand like images that are commonly found on user smartphone and computerdevices in large number but interfere with automated selection systemsand methods by unnecessarily increasing the total set of images to beautomatically analyzed and diluting the benefits of the automated imageselection systems when these meme images make it into final resultsbecause they are confused for system user generated content/images.

Along with the de-duplication process, the data from the personalizationfunnel goes through analysis in the form of static and othermachine-learning dimensions, and algorithmic dimensions, to createnormalized data across attributes like the subjective aesthetics, thesubjects of the content, the mood, or the composition of the content.This entails the generation of single values equaling a plottablelocation in the system's volumetric hyperspace analysis such that asingle point can be plotted (x:1, y:2, z:3, . . . n:1.6). A nexus ofmeaningfulness, or idealized personal center represents the relativecenter of the volumetric analysis. A magnitude filter compares thelocation of all the individual assets to the hypersphere boundary toremove those images/set from the final selection set.

The invention originally relied on a static hypersphere boundary, theboundary definition aspect of the system has been modified to include auser interaction that changes the hypersphere boundary location afterinput from the user that tells the system to expand or contract thevolume of the hypersphere. This user interaction can take many formsincluding but not limited to choosing a percentage of total content, amaximum number of content assets or an incremental input that allows theuser to slowly expand and reduce the hypersphere volume. Additionally,the invention now allows for finding the center of the intersection oftwo or more hyperspheres to allow for filtering or combining multipleusers together within the same space.

After the final set of images/assets have been selected, the systemgenerates an explanation in natural language for why it predicted thatthe final set of images are the most meaningful. This feedback to theuser is novel in that prior art based on Machine Learning is mostly ablack box process where the logic of the system decisions is hidden ornot understandable by system creators of users. The current inventionprovides a superior product in that it can now give direct feedback touser regarding the images qualitative and quantitative meaningfulproperties. The system can then present an optimized set of images tothe user along with natural language feedback so that the user canreview the feedback and provide their own counter feedback on the imagesproposed to him/her by the system.

There has thus been outlined, rather broadly, the more importantfeatures of the invention in order that the detailed description thereofmay be better understood, and in order that the present contribution tothe art may be better appreciated. There are additional features of theinvention that will be described hereinafter, and which will form thesubject matter of the claims appended hereto. The features listed hereinand other features, aspects and advantages of the present invention willbecome better understood with reference to the following description andappended claims. The accompanying drawings, which are incorporated inand constitute part of this specification, illustrate embodiments of theinvention and, together with the description, serve to explain theprinciples of the invention.

It should be understood that while the preferred embodiments of theinvention are described in some detail herein, the present disclosure ismade by way of example only and that variations and changes thereto arepossible without departing from the subject matter coming within thescope of the following claims, and a reasonable equivalency thereof,which claims we regard as our invention.

BRIEF DESCRIPTION OF THE FIGURES

One preferred form of the invention will now be described with referenceto the accompanying drawings.

FIG. 1 is a work flow diagram showing the basic components of the systemwith a brief description of each component and its function.

FIG. 2 is another work flow diagram showing the section of the inventionthat controls the identification and removal of duplicate andnear-duplicate images.

FIG. 3 is a work flow diagram that focuses on the general contentperception/personalization funnel.

FIG. 4 is yet another work flow diagram showing the hyperspherevolumetric analysis portion of the invention.

FIG. 5 is yet another work flow diagram showing the SubjectiveComposition Analysis System

FIG. 6 is a work flow diagram showing the Similarities Detection SystemFlow system.

FIG. 7 is a work flow diagram showing the Realtime Evaluation System.

FIG. 8 is a work flow diagram showing the Personalized ContinuousLearning System component of the invention.

FIG. 9 is a work flow diagram illustrating the New Media Flow.

FIG. 10 is a system flow of an embodiment similar to the one illustratedin FIG. 1.

DETAILED DESCRIPTION OF THE FIGURES

Many aspects of the invention can be better understood with referencesmade to the drawings below. The components in the drawings are notnecessarily drawn to scale. Instead, emphasis is placed upon clearlyillustrating the components of the present invention. Moreover, likereference numerals designate corresponding parts through the severalviews in the drawings. Before explaining at least one embodiment of theinvention, it is to be understood that the embodiments of the inventionare not limited in their application to the details of construction andto the arrangement of the components set forth in the followingdescription or illustrated in the drawings. The embodiments of theinvention are capable of being practiced and carried out in variousways. In addition, the phraseology and terminology employed herein arefor the purpose of description and should not be regarded as limiting.

The system comprises four basic elements, as illustrated in FIG. 11: AGeneral Content Perspective (800), an Individual Content Perspective(801), a Personalized Content Presentation & User Feedback (802), and aHypersphere Analysis (802). The details of each of the four basicelements along with their interaction with each other is detailed belowin several embodiments.

FIG. 1 is a work flow diagram showing the basic components of the systemwith a brief description of each component and its function. FIG. 10 isa system flow of an embodiment similar to the one illustrated in FIG. 1.Both Figures will be discussed in this section. The system comprisesfour basic elements as is clearly detailed in FIG. 10: A General ContentPerspective (800), an Individual Content Perspective (801), aPersonalized Content Presentation & User Feedback (802), and aHypersphere Analysis (802). The details of each of the four basicelements along with their interaction with each other is detailed below.

The system begins with the Content and Media Users (1) In a preferredembodiment, the system user is a person who has photos they havepersonally taken stored on a computer device, however, the “content andmedia users” (1) could be a variety of people, groups or institutions.

The first part of the system involves a Motivational Perspective Survey:System, which surveys users with a series of behavioral questions toexplicitly learn about the user, their behavior and motivations. For theexample of photo selection, these surveys could cover whether the personprefers landscapes or portraits, color or black and white, etc. Theanswers to the survey allow the system to determine what mix of systembase motivational personas to assign the individual user. E.g. theuser's responses might indicate that the user aligns 90% to the SoccerMom persona, 6% to the Social Snapper and 4% to the Passive Photog. Eachpersona has a baseline behavior, preference and aesthetic tendency whichaffects the system probability modelling and decision making. Note: thisstep can also be accomplished by the system analyzing the user's photocollection to infer their personality/motivational persona mix insteadof a survey to make the determination of persona mix via (07) ImplicitLearning). This survey establishes the learnings in (05) the PersonaModels.

The Implicit Learning element (07) improves system performance in avariety of ways, in addition to allowing the system to function withoutexplicit learning. One of which, I will be using as an example in thisUtility patent. Through Implicit Learning (07) in the PersonalizationFunnel (03) the invention uses new content coming into the system toimprove the overall system's ability to analyze new content for specificsubjective meaningfulness before the new content in question is analyzeditself. The content of the implicit learning element cross-transmitsinformation with the Subjective Composition element (51), such that theUniversal Model (4) communicates with the upper level (4A) of thePersonalization Funnel (03), the Persona Models (5) interact with themiddle level (5A) of the Personalization Funnel (03) and the IndividualModel (6) cross-communicates with the bottom level of thePersonalization Funnel (03). This method of improving the system'sunderstanding of the invention user's generalized subjective perspective(03) through ongoing feedback and improvement, and therefore performancewith content (02) the system will later analyze for specificmeaningfulness, including a static dimensions element (12), anothermachine learning dimensions element (13) and an algorithmic dimensionselement (14), that is not seen in prior art and provides a significantimprovement over the existing technology.

To illustrate, when the (02) Input Plurality of Content Media eventoccurs and the content first interacts with system components in thePersonalization Funnel (03), the images are analyzed but not to producemeaningfulness predictions about the content being evaluated. Rather,the system first analyzes the content with the Personalization Funnel(03) models to look for patterns, concepts, motivational connections andmeaningful subject matter, et al, in a reinforcement process to informthe Personalization Funnel models themselves through the ImplicitLearning (07). As an example of how the system uses Implicit Learning(07) to improve automated selection of content in a way that prior artfails to achieve, we start with a collection of photos being analyzedfor meaningfulness in the preferred embodiment. For this example, whenthe (02) Input Plurality of Content Media event occurs, thePersonalization Funnel (03), which houses the system user's GeneralContent Perspective models, receives input that includes a highpercentage of photos with children in them, a majority that includeimages from soccer games with children and multiple but not a majoritythat include a new repeated face.

The Implicit Learning (07) system of the invention will recognize thesequalities and update the Personalization Funnel(03) models to improvethe system's understanding of the user's General Content Perspective byreinforcing the concepts of children, the system user's own children,soccer, fields, action, cheerful moods, social cooperation and outdoorlighting, that are already embedded in the example system user'sIndividual Models (06), through prior explicit and implicit learning,and their base Persona Model (Soccer Mom). The Implicit Learning in thisexample updates the Subjective Composition element (451), which includesSubjective Composition element (451), which, in turn, comprises theUniversal Model (4), which communicates with the upper level (4A) of thePersonalization Funnel (03), the Persona Models (5), which interact withthe middle level (5A) of the Personalization Funnel (03) and theIndividual Model (6) cross-communicates with the bottom level of thePersonalization Funnel (03)with the corresponding relative strength ofreinforcement to incorporate the distribution of the qualities in thecontent set being currently evaluated.

This Implicit Learning (07) process then improves the PersonalizationModels before the (02) Input Plurality of Content Media is examined formeaningfulness in the Personalization influenced processes a staticdimensions element (12), another machine learning dimensions element(13) and an algorithmic dimensions element (14). The invention improvesover prior art by using new content in a novel way to improve theselection of content downstream in the invention.

The original invention relied on a sequence of behavioral surveyquestions and now the system has been modified to also include quickset-up modes that allow the system user to choose a photograph from aset whose qualities imply many of the answers to the behavioral surveyquestions. This allows the user to be assigned to a base persona soonerand more efficiently.

Part 2—Individual Perspective Image Survey: System surveys user using aseries of images to collect subjective perspective data to explicitlylearn about the user, their personal perspective on aesthetics, content,mood, composition and sub-qualities selected by the personarepresentatives. These learnings about the individual subjectiveperspective work as an overlay to the base persona and universal photolearnings and transform the workings of the system from a system capableof making predictions based on known general preferences andmotivational biases to a system capable of making predictions based onthe specific subjective tendencies influence by broad motivationalbiases. This survey establishes the learnings in the Individual Model(06) and continues to be available at any time for system users who wishto continue to interact with the explicit learning mechanism.

Continuous Learning and Improvement (9) As soon as the explicit learning(8) is complete, the system enters a state of continuous learning andimprovement where it takes in data and learnings from multiple feedbackpoints to improve itself via improvement of the superset of novel modelsrepresenting of the system user perspective. It should be noted that thesystem can work without the (8 Explicit Learning) and go right intoContinuous Learning and Improvement (9) element to learn about the userperspective from use of the system instead of direct inquiry or surveyof the user.

At the end of the first cycles of Continuous Learning and Improvement(09) a new instance of the system AI is established using the universalmodel, base persona model and individual perspective learnings to form asystem comprising a superset of novel models, i.e. the PersonalizationFunnel (03) or General Content Perspective system, that thereafter isrepresenting of the system user perspective. This new instance of theinvention AI is a unique system where learnings about one individual areembedded and unlike any other instance as soon as the individualperspective learnings are overlaid on the base persona mix. This is thesystem that is now ready for content analysis from the user'sperspective and continuous improvement.

Time (10) Time has a special function in the system and is constantlyaltering the user perspective models based on a modified inverted bellcurve where very recent images/events identified in images are judged asprobably more meaningful to a person than images/events slightly fartheraway in time with a reversal as images/events identified in images getfarther away in time where, over a long period of time, images/eventsidentified in images are judged as probably even more meaningful thanrecent events once they reach a specified threshold of distance in thepast. The original invention relied on Time to help shape the system'sprediction of how meaningful content will be throughout time. Theinvention has been modified so that as individual personalized modelsevolve over time through learning, the system can also follow trends inpersonal preference to predict how a user's subjective analysis willcontinue to change in the future as well even if the user has notcontributed new data to the system for a period of time to help thesystem learn.

Context (11) The context detected in images or explicitly requested ortagged by system users is also part of the continuous learning loop asthe system uses the context identified in images to alter the expressionof behavioral and preference biases in the models. That is, if abirthday party of a child known to the user is detected in the imagecontent, that context will enhance the probability of meaningfulness tosystem users aligned most strongly with the Soccer Mom base motivationalpersona. In addition, personalization can be influenced directly bycontext, providing both filtering and fundamentally different (15)Normalized Data) based upon contextual preference. That is, asking forthe best photos for a photobook of a wedding for grandma fundamentallyshifts the personalization favoring photos better printed vs. digital,filtered by the wedding, and taking into account grandma's preferencesas learned from the system user.

The system, using the models in Universal Model (04), Persona Models(05) and Individual Models (06) as a base to represent the userperspective, is now ready to analyze images and select the mostmeaningful images in proxy for the user.

Input Plurality of Content/Media (02)) Load Image into Computer VisionSystem Apparatus—Images are now uploaded or input into the system. In apreferred embodiment, a plurality of images in the hundreds or thousandsis input into the system. Additional images can be added one by onethrough a camera function of the preferred embodiment or uploaded assingle images or in batch after the initial library is imported.

The system uses the models in the Personalization Funnel (03) containingthe models Universal Model (04), Persona Models (05) and IndividualModel (06), and machine learning, image metadata, computer vision andalgorithmic processes to make probabilistic predictions about the imagesacross a plurality of dimensions including the reference dimensions inUniversal Model (04), Persona Models (05) and Individual Model (06) andStatic Dimensions (12), Other Machine Learning Dimensions (13), andAlgorithmic Dimensions (14). These dimensions and values make up the(Individual Content Perspective). The dimensions include image qualitieslike the predicted subjective perspective of the user regarding theimage, objective measurable dimensions such as time the photo was taken,and content detected in the image.

During Static Dimensions (12), Other Machine Learning Dimensions (13),and Algorithmic Dimensions (14), a multi-modal system process for imageDe-duplication takes place. This novel process uses the mixed modelalgorithmic and of Machine Learning analysis to determine which imagesare probably duplicate or near-duplicate so as not to include too manyimages in the final selection that are images in a series where theimage creator, the system user, captures many instances of the samesituation.

Once the plurality of dimension values for system user's perspective(General Content Perspective) and plurality of dimension values for theimage (Individual Content Perspective) are predicted the two sets ofvalues are independently normalized (15) into separate single values theequaling a plottable location in the systems volumetric hyperspaceanalysis.

The volumetric analysis is set up with the (General Content Perspective)output represented by (17 Idealized, Personal Center)((the nexus ofmeaningfulness)) as the center of a hypersphere in many dimensionalspace, the (Individual Content Perspective) for a single imagerepresented by the (18 Individual Asset) location and a boundary of thehypersphere representing the divide between highly meaningful and lessmeaningful images—represented by automatic or user selected thresholds.

Magnitude Filter (20) is where the system compares the location of all(18 Individual Assets) to the hypersphere boundary to remove thoseimages/assets (19) Individual Asset Plotted Outside of Hypersphere) fromthe final selection set.

Explain Dimension and Magnitude Relationships/Natural LanguageGeneration (21). After (20) a final set images/assets have been selectedand the system generates an explanation in natural language for why itpredicted that the final set of images are the most meaningful. Thisfeedback to the user is significant in that prior art based on MachineLearning is mostly a black box process where the logic of the systemdecisions is hidden or not understandable by system creators of users.We have made overcome this challenge for many content characteristicsand can now give direct feedback to user regarding the imagesqualitative and quantitative meaningful properties. This is in partpossible because the system user perspective embodied in the(04)(05)(06) models is used again in (21) to influence how the systemfeedback is generated so that it matches the individual user'spersona/subjective perspective in tone and content.

Present Final Set of Images and Explanation of System Decisions to User((22) the system apparatus presents the final set of images and a set ofnatural language feedback to the user for consumption and interaction.

Explicit and Personalization Feedback Loop (23) User actions concerningthe images after the final set of images is presented to the user andfeedback regarding the image selection/system explanations is fed backinto the system as part of (9 Continuous Learning and Improvement).

After the Present to User for Content Meaningfulness Human Interactionselement (22) the system can load more images into system, or systemusers may continue to interact with the system through explicit learningor contextual input.

FIG. 2 is another work flow diagram showing the section of the inventionthat controls the identification and removal of duplicate andnear-duplicate images. A set of images is put through a number ofassessment systems which determine an algorithmic difference 24, analgorithmic distance 25, feature similarities 26, resulting in a visualmachine learning model 27. These assessments fit into an ensemblelearning 30 component, which directs both a positive series 31 and anegative series 32, and a positive short circuit 28 and a negative shortcircuit 29.

While pixel difference and pixel distance algorithms are commonly usedfor photo copyright detection, this system uses them as tools ininterpreting machine learning and algorithmic analyses of content. Thishelps provide guidance for identifying very nearly identical series ofphotos or just very similar photos that may have substantial overlap innon-exact pixel features for the sake of adding penalties or rewards asappropriate to feature dimension weights.

(24) A section by section analysis of direct pixel differences, atechnique frequently used to check for copyright infringement.

(25) “Perceptual” distance, compiling two vectors from both images andcomputing a perceptual distance. Also, frequently used for copyrightinfringement detection but is “fuzzier” in the calculation and candetect if two images are close, but there was a sudden lighting change,for example.

(26) Feature Similarities evaluates pictures taken in close successionfor a broad range of very similar features. Are the same objects inframe? Same faces identified? The same predominant color palette and isobject composition similar?

(27) We also trained and employ (in one form of implementation) aConvolutional Neural Network (CNN) trained specifically on commoncategories of series photos.

(28) If one model in the pipeline has very strong, positive confidence,we short circuit further processing and return a positive match.

(29) If one model in the pipeline has a very strong, negativeconfidence, we short circuit further processing and return a negativematch.

(30) If no one model had strong positive or negative confidence wecombine all of their results into an additional machine learning modeldesigned to make a final decision.

(31) Positive match for an image in the series includes the uniqueidentifier of each, other image detected.

(32) Negative match for an image in the series also includes the uniqueidentifier of each, other image detected.

By way of additional explanation, in this particular embodiment theimage de-duplication system employs methods that combine image analysistechniques well known in the art in new and unexpected ways to identifyand remove duplicate and near duplicate images from the final selectionset. This is crucial to ensure that the automated content/imagepersonalization and curation system doesn't include too many images inthe final selection that are the same or almost the same because doingso dilutes the benefits of the system, as is seen in prior art This isespecially beneficial in systems intended for use by everyday people inthe modern world because it is well known in the art that photocollections created by system users, e.g. the photo library on anysmartphone, contain many near duplicate images because:

There is a common tendency to take repeat shots to get just thecombination of qualities seeking the perfect picture.

The ease of taking photos with modern cameras and smart phones.

However, the system user does not want ALL of these identical or nearlyidentical images; only the “best of the bunch/most meaningful”.

Prior art systems and the techniques known in the art recognize thisproblem but provide no systems or methods to solve for the problemexcept using readable metadata from images and comparing to the metadataof other images to identify duplicates by time of day taken and otherobjective data recorded in the image files header. Using metadata tosolve this problem produces inconsistent and poor results becausemetadata is not consistent across images, cameras or users and thistechnique can only identify actual duplicates not near duplicates whichare a bigger problem in automated content and image selections fields.The current invention solves this problem by combining known techniqueswith new techniques of the inventors' design in a multistep, multiapproach system that produces benefits unachievable using prior art andtechniques. Traditional techniques, like those used to identifycopyrighted images using direct pixel comparison and meta data, produceresults which do not remove all duplicates and have no ability to detectimages in a series which are part of a bigger series taken in close timeproximity to each other. The current system is designed to solve themore complex problem of series and near duplicate detection using thetraditional techniques as a first analysis layer, to quickly removeimages that are easy to identify as duplicates and don't require theheavier computational load of our newly designed techniques.

The next layers of the de-duplication analysis system uses transferlearning techniques and experimentation with the outputs of variousMachine Learning models not designed for finding duplicates, includingdata from object detection ML model processes that are halted at randomtimes to gather image hash data for comparison to the images beinganalyzed for the presence of duplicates and the use of a ConvolutionalNeural Network trained on common categories of series photos. Thesequenced combination of simple duplicate identification techniques fromprior art and the more computationally complex techniques derived fromexperimentation can identify duplicates and the more important nearduplicates from a large set of photos taken by a typical system user orconsumer of today with a smartphone with a high level of accuracy unseenin prior art.

FIG. 3 is a work flow diagram that focuses on the general contentperception, or GCP/personalization funnel. This part of the systemlearns from implicit and explicit analysis of the user's content andbehavior, then models how a system user generally perceives content,e.g. what the user finds meaningful across objective and subjectiveimage dimensions. It's important to note that the GCP is theself-improving record of how the system user perceives content/images ingeneral; the personalization funnel is the mechanism by which the systemapplies that understanding when analyzing new content/images to predicthow meaningful the image would be to the system user.

An objective base helps to filter and frame subjective learning. Forexample, detecting photos accidentally taken with full or no exposure(all black or all white) is universally able to be rejected withoutwasting further resources.

(35) Subject matter experts, such as professional and acclaimedphotographers, provide further universal photographic feature learning,such as composition and color basics.

(36) Universal models are trained out-of-band to system operation(external), with small nudges that are not too disruptive to thefoundational models, avoiding a large cascade effect.

(37) Quantitative universal base provides no subjective traits and istherefore only half of the total universal model's composition.

(38) In conducting ethnographic research on subjective features forcontent, some universal standards emerge across every demographic andpersona group.

(39) Universal qualitative training is captured, also with small andincremental nudges to overall learning as part of an external andongoing system improvement process.

(40) Participants for persona learning are selected through surveys.

(41) Persona categorized research subjects provide supervised systemlearning in an external, ongoing, and incremental basis.

(42) Individual system users provide ongoing, incremental explicit andimplicit learning through use of implicit content and explicit trainingfunctions.

(43) The resulting biases and weights produced from the funnel ofpersonalization can be used and reused throughout the overall contentevaluation processes.

The GCP system's first layer is learnings about universally aestheticvalues, e.g. preferred aspect ratios, and, technical properties, e.g.contrast levels, common in a wide variety of photography that can beassumed to be shared by most everyone as they dominate art, film andphotography across the record of these media. This universal learning issupervised and aimed at teaching the system about the qualities peoplegenerally agree are present in examples of exceptional and pleasingphotographs. This layer forms the basis of all the individual AIinstances, i.e. the system as it is established for an individual user.

The initial invention relied on universal aesthetics composition systemsand models from the consumer off the shelf (COTS) software market thatare used to predict objective composition qualities of images in anaggregate fashion. These systems and models were transformed in theoriginal invention into a system capable of subjective analysis ofobjectively measurable qualities by the effect of the personalizationfunnel (03). Now the invention has been modified with a compositionsystem which analyzes content along various specific aestheticcomposition dimensions through the lens of the invention user'ssubjective perspective as well as generative “idealized” compositionmeasures as trained through the collection of subject matter expertfeedback on core composition qualities to predict granular independentqualities of the content and combines them back together to create anovel aggregate composition prediction.

As with other systems of the invention, the compositiondimensions/qualities predicted by the invention are used because they,with the specific overlay of subjective perspective, best predict thecompositional value of a photo to an invention user.

The invention's composition modification has multiple advantages overprior art. First, prior art focuses on aggregate composition qualitiesthat doesn't allow for individual outstanding qualities to beconsidered, the current invention improves on prior art by recognizingthe layered and multi-faceted nature of visual composition. Second,prior art doesn't allow for individual outstanding qualities that aremore predictive of content value to an individual invention user to beamplified by subjective bias of the invention user, the currentinvention does allow individual outstanding qualities that are morepredictive of content value to an individual invention user to beamplified by subjective bias of the invention user making the contentselection process of the current invention inherently more accurate thatprior art. Third, prior art composition systems are analysis onlysystems and have no generative features to improve the image analysisand selection systems outcomes, the current invention analyzes new mediato predict its compositional qualities and then generates a new novelpiece of content geometry and then compares the predicted compositionalqualities of an image the system generated ideal version of the samecontent geometry to add precision to the inventions compositionalquality predictions. Lastly, prior are being aggregate based systems, donot permit individual dimensions and their strength to influence theperception of other independent content qualities because prior aremakes one prediction about content composition where the currentinvention uses every dimension and the predictions of the system toinfluence downstream and sometimes previous dimensions in a recursiveprocess to improve the precision of dimension predictions on acontinuous basis. The modified composition system is active in thecreation and continuous improvement of the GCP universal models, thepersona models and the individual models and, of course, in evaluationof new media/photos in the preferred embodiment.

The next layer of the GCP is the persona model which is a set ofbaseline perspective models that represent default behaviors and biaspatterns common to people who share a core motivation. For instance, apreferred embodiment has a Social Snapper motivational persona modelthat system users can be associated to via a survey or image analysisprocess. Persona models are trained by interacting with users that arepre-identified as being motivated by status and social competition. Whenthe system user is determined to align to the Social Snapper persona,the system overlays the Social Snapper behavior, preference andperspective biases on top of the universal models so that the systemwill now understand, for example, that images containing the userthemselves, which have high clarity and close cropping are moremeaningful to the user than images that do not have thesequalities/these qualities in combination. In a preferred embodiment asdescribed in FIG. 3, the system has multiple baseline motivationalpersona models. A system user is assigned a percentage mix of two ormore of the persona models. This mix of personas better mimics the humancondition where people and their behavior/decisions are driven by a mixof motivations where the relative influence of any one motivation onuser behavior/decisions changes based on context as well as othersituational factors.

The final layer in the GCP is the individual model layer where thesystem learns and records the user perspective across a variety ofsubjective image perception dimensions including motivation, mood,subject matter, composition, color, lighting, cadence, photography bestpractices, aesthetics, etc.

The output of the GCP is a set of Personalization Weight and Bias valuesthe system predicts that allow the invention to apply all the learningsrepresenting the user perspective to image personalization and curationtasks.

Objective Universal Training 33 provides a static analysis 34 and asubject matter expert supervised machine learning function 35. Theseallow for incremental, external machine learning 36 which creates aquantitative universal base 37, which is fed into the personalizationfunnel. The first layer of this funnel is the subjective universalrepresentation 38, which allows for incremental, qualitative machinelearning 39 which feeds into the universal model 4. The next “layer” ofthe funnel is a subjective persona representation 40, which containsimplicit learning from system use 7, which in turns helps to create theuniversal model 4. The subjective persona representation 40 alsoincludes explicit survey taking persona(s) categorization 8 which allowsfor grouped personal supervised machine learning 41, which in turn helpsto create both a personal model 5 that feeds into the first layer of thepersonalization funnel and the personal model 5 that feeds into thesecond layer.

FIG. 3 also illustrates the subjective individual system user 42component, which in addition to feeding into the implicit learning fromsystem use 7 component, also contains several additional components. Anexplicit survey taking persona(s) categorization 8, an explicit 3^(rd)party content evaluation 8 and a personal system use implicit learning 7component each feed into successively “lower” layers of thepersonalization funnel. The result of all these components is apersonalization weights and biases 43 component, which is used topredict which images from a set of images a user will find meaningful.

FIG. 4 is yet another work flow diagram showing the hyperspherevolumetric analysis portion of the invention. This figure illustrateshow the various components that result in normalized dimensions 48 arecombined, and the role of the hypersphere plot 16. From the ML Model(s)44, a machine learning dimension 13 helps to create dimensions prior tonormalization 47. Also contributing to the dimensions prior tonormalization 47 is a static dimension 12 created from formulas andstatic attributes 45 and an algorithmic dimension 14 created from sourcecode 46.

Also contributing to the process is an ensemble machine learningdimension 13 which is created from a combination of a machine learningdimension 13, a static dimension 12 and an algorithmic dimension 14. Thevarious dimensions then go “into the personalization funnel” where apersonalized weighted normalization 15 function occurs, resulting in anormalized dimension 48.

The one or more normalized dimensions 48 go through the hypersphere plot16 process, which includes an idealized personal center (Nexus ofMeaningfulness) 17, and results in an individual asset 18, whereupon theindividual asset can be plotted outside of the hypersphere in a“individual asset plotted outside of the hypersphere” 19.

Machine Learning (ML) models are any models which can adapt and learnthrough either normal system use or explicit training cycles. Commonexamples include computer neural networks, LSTMs, CNNs, etc. Theseindividual tools work together or independently to satisfy answeringone, and only one specific question as a range of probability, packagedas one dimension of the hypersphere (a sphere with N-dimensions greaterthan 3 defined with a center point of the ideal, personal center ornexus of meaningfulness and a radius representing the limit for contentqualification).

(45) Formulas and Static Attributes represent source code and metadatawhich does not change and needs not learn or adapt. For example, thetimestamp of a photo or the top two predominant color averages within agiven range. These are converted into dimensions that can be weightedand normalized according to personal preferences.

(46) Source Code powers algorithmic dimensions that improve over time,but only through explicit code updates through external processes to therunning system. For example, calculating the horizon and rotation of animage may include algorithmic edge detection followed by a series ofanalyses which can continuously be improved upon.

(47) Dimensions Prior to Normalization shows how complex normalizationcan be with a wide variety of types of dimensional plots. Normalizationstitches together graph gaps, reverses and flattens exponential orlinear stretching, and much more so that the sphere can be fullysymmetrical and so that personalization and learning can drive directchange to the axis used in plotting and analysis.

(48) Normalized dimensions appear fully uniform and are ready forplotting content.

FIG. 5 is yet another work flow diagram showing the SubjectiveComposition Analysis System. FIG. 5 shows how, in the preferredembodiment of the system, the system predicts the compositionalqualities and strength of those qualities of content across a variety ofmeaningful aesthetic dimensions through the lens of the invention user'ssubjective visual perspective. The visual composition of media is anintegral factor when evaluating the subjective meaningfulness conveyedby media because of its universal applicability to visual contentperception. Composition and the ability to recognize and predict/measurethe strength of accepted composition elements, like subject matterbalance and framing, is transformed in the system, using the user'ssubjective perspective, into a predictive factor of increasedimportance. The current invention, unlike prior art, can recognize notonly good or bad composition qualities in media but also if the mix andstrength of the composition qualities in the media will be specifically,subjectively meaningful to the individual invention user.

In FIG. 5, it is important to note, the objective composition analysisis aided by a novel process of data transformation where the analysisoutputs are determined through a generative process. This systemattempts to define the idealized form of a given type of composition inreference to the content being analyzed and then uses statistical rulesto determine how far off the actual piece of content is from thatidealized form. Subjective composition, by contrast, is predicted via aprobability influenced both by the personalization funnel, models thatcome before it, and any other analysis data points previously saved asmetadata to the media, such as the focus of the content or the pitch andangle of the framing.

The Singular Media for Analysis (49) Depicts media ready for analysis.This is content ready to be analyzed.

The Object Detection Model (450) shows how some prior model data feedsforward to help inform the Composition Analysis model, such as theObject Detection Model. Static and algorithmic features such as overallmedia visual clarity, object detection, camera type, date of mediacapture, and any number of other factors can feed into a learning systemthat constantly improves in its subjective and objective classificationsof composition.

The Region Layer Model (51) shows, again, how some prior model datafeeds forward to help inform the Composition Analysis model, such as theRegion Layer Model which identifies large continuous regions assistingmany boundary attributes within content such as foreground vs backgroundor surface detection. Focus Detection (53) is also used in thecompilation of data.

The Clarity, Pitch, Roll & Yaw Detection element (52) shows how someprior static, algorithmic, or learning models are used to feed forwarddata, helping inform Composition Analysis. For example, Pitch, Roll, andYaw detection provides detected orientation data on the camera used tocapture content.

The Focus Detection element (53) shows how some prior static,algorithmic, or learning models are used to feed forward data, helpinginform Composition Analysis. For example, Focus Detection provides a“blur” index on different important regions of the content.

This is where the Personalization Funnel once again plays an importantpart in defining the subjective elements of forward models. ThePersonalization Funnel touches nearly every process and composition isno exception. The flow from composition model to composition model isweighted and interpreted by personal preferences for subjectivefeatures.

In the Depth Composition Classifier (54), the Symmetry CompositionClassifier (57), the Golden Ration Composition Classifier (59), the FillFrame Composition Classifier (61), the Balance Composition Classifier(63) the Additional Composition Pair Classifier (65) and the PureSubjective Composition Classifiers (67), a set of separate subjectiveand highly personalized set of models come into play.

The first is the Depth Composition Classifier (54), this model of a corecomposition concept helps to inform what a system user would personallyfind meaningful and aesthetically aligned to their personal visualperspective. It is important to note the difference because even basiccompositional elements, like use of pattern or texture can beemotionally meaningful without being aesthetically pleasing nor intendedas aesthetic elements in visual content. For example, objectively DepthComposition can be defined but whether that Depth Compositionsubjectively helps or hurts the content in question is subjective andpersonal in nature.

In the Depth Composition Generator (55), the Symmetry CompositionGenerator (58), the Golden Ratio Composition Generator (59), the FillFrame Composition Generator (61), the Balance Composition Generator (63)and the Additional Composition Pair Generators (65) a set of separateobjective models of a core composition concept has been created throughextensive system learning from subject matter experts, such asprofessional photographers, who have well defined “universal”definitions of what represents or doesn't represent a common compositionconcept.

We see this first in the Depth Composition Generator (55), which helpsto evaluate Depth via the generation of an ideal composition based onthe induvial piece of content based on analysis of foreground andbackground elements.

The Models Merge to Singular Dimension (56) depicts how the objectivecomposition elements are combined and interpreted by their mirrorsubjective composition elements to create a comprehensive analysis ofthat form of composition, fed forward through the model pipeline tohelp, where applicable, in informing forward models.

The Symmetry Composition Classifier (57) shows a core compositionconcept helping to inform what a system user would personally findmeaningful. For example, objectively Symmetry Composition Generator (58)can be defined but whether that Symmetry Composition subjectively helpsor hurts the content in question is subjective and personal in nature.

Symmetry Composition Generator (58) shows an objective model of a corecomposition concept, Symmetry Composition can be evaluated for a givenpiece of content based on analysis of the primary subjects or objects ofthe content and how they are framed. Results of previous compositiontypes can influence new composition models.

Golden Ratio Composition Classifier (59) is the layer in compositionanalysis where an objective model of a core composition concept, theGolden Ratio Composition Generator (60) resides and can determine thealignment of the content to the idealized Golden Ratio geometry for thespecific piece of content's aspect ratio. This alignment can be definedbut whether that Golden Ratio Composition subjectively helps or hurtsthe content in question is subjective and personal in nature.

Golden Ratio Composition Generator (60) depicts an objective model of acore composition concept, the Golden Ratio Composition, can be evaluatedfor a given piece of content based on analysis of object internalalignment Golden Ration geometry and framing within the content.

The Fill Frame Composition Classifier (61) shows a core compositionconcept helping to inform what a system user would personally findmeaningful. Fill the Frame Composition Classifier can objectivelydetermine Fill Frame Composition Generator (62) but whether that FillFrame Composition subjectively helps or hurts the content in question issubjective and personal in nature.

Fill Frame Composition Generator (62) shows an objective model of a corecomposition concept, Fill Frame Composition can be evaluated for a givenpiece of content based on analysis of the primary subject detected andits framing within the content.

The Balance Composition Classifier (63) shows a core composition concepthelping to inform what a system user would personally find meaningful.Objectively, Balance Composition Generator (64) can be defined butwhether that Balance Composition subjectively helps or hurts the contentin question is subjective and personal in nature.

Balance Composition Generator (64) shows an objective model of a corecomposition concept, Balance Composition can be evaluated for a givenpiece of content based on analysis of object layout within the framingof content.

The Additional Composition Pair Classifiers (65) shows a corecomposition concept helping to inform what a system user wouldpersonally find meaningful. Additional Composition Pair Classifiers (65)in FIG. 5 specifically calls out that this pattern can repeat for anynumber of composition concepts where subjective models can achieve alevel of meaningful predictive fidelity.

The Additional Composition Pair Generators (66) show an objective modelof a core composition concept, Additional Composition Pair Generators(66) in FIG. 5 specifically calls out that this pattern can repeat forany number of composition concepts if they can be objectivelyclassified.

The Pure Subjective Composition Classifiers (67) depict how additionalSubjective Composition Classifier models are used which have nocurrently known objective classification to mirror, but which stillprovide useful subjective meaningfulness analysis of a given piece ofcontent.

The Multi-Dimensional Weighted Result (101) depicts how all thecomposition model analysis are compiled into dimensions representing thesubjective meaningfulness of the overall composition concepts to beplotted and normalized separately, as FIG. 5 represents just oneensemble model within the larger content evaluation system. It'simportant to note, Compositional dimension value is can be in the formof either a probability or can be created through composition generatorspredicting ideal versions of composition based upon clip coordinatesthat are then compared with the existing media's form, providing ascalar representing delta.

Mirror Categories (100) Illustrate how many objective compositioncategories have mirrored subjective composition types.

FIG. 6 is a work flow diagram showing the Similarities Detection SystemFlow system. FIG. 6 shows the preferred embodiment of the system thatpredicts which of the similar photos would be the most meaningful to theuser when compared to the other images/content in a set of similarcontent. Duplicate and lesser quality near-duplicate contentidentification is critical to the accurate and precise selection ofmeaningful content. Systems that do not identify and remove or demoteduplicates and lesser quality near-duplicates will inherently produceinferior content selection results. That is, the purpose of a selectionsystem is to identify a subset of content from a larger set. Thisinherent principle of selection means that the output of a selectionsystem is a finite set culled from a larger set. The presence ofduplicates and lesser quality near-duplicates in the finite selectionset pushes out content that otherwise would have been in the result setwhere the duplicate or lesser quality near-duplicate appear. Prior artmakes some attempt to remove duplicates through meta data comparison,which is in turn is inherently flawed compared to the currentinvention's visual duplicate identification method, but, prior art doesnot consider the more serious problem of near duplicates at all. Thedilution that results from this omission in prior art impairs priorart's ability to make highly accurate selections and instead shows theuser the same or nearly same but less attractive content repeatedly incontent sets where it is common to have duplicate and near duplicatecontent exist side by side. Note, the invention's ability to select thebest of near-duplicate content produces benefits for commercial uses,e.g. finding the best marketing image from a photo shoot collection,that prior art also fails to consider or solve.

FIG. 6 shows how the system identifies duplicate and near-duplicateimages and uniquely selects the best of a series of near duplicatecontent/images using the subjective perspective of the system user as aguide to selecting the best of the similar images. The invention marks asignificant improvement over prior art by improving the ability of theinvention to automatically select meaningful content for system userswithout repeating or nearly similar content diluting the results.

Visual Media to Compare (102) depicts Media for Analysis. This iscontent ready to be analyzed.

Static Media Attributes (103) is where static attributes attached to theVisual Media to Compare (102) are extracted. These include when themedia was recorded, geolocation, it's resolution, the media capturedevice or in the case of photos, the “exif” meta-data includingmanufacturer specific attributes. These limited and inconsistent mediaattributes are often the main attributes used in prior art whereas thecurrent invention recognizes that these attributes are important as abase but not sufficient for the overall selection task.

Weighted Alternate AI Models (68) shows the Similarity detection methoddepicted in FIG. 6 is one part of many AI (or algorithmic) models whichfeed forward their data for analysis in this similarity model. Forexample, object detection, color analysis, and any number of otherrelevant processed data may feed forward into this model for its use indetecting duplicate or similar content. By feeding in the results ofprevious models in the system flow, the invention improves theprediction accuracy of the similarity system and models. For example,perhaps the setting and poses are the same, however a photo or videoincludes different people at the same event. These may deserveindependent evaluation, influenced further down the line byindividualized preferences and other factors.

AI Similarity Model (104) show the system's core Similarity Model, thissubsystem is AI in nature due to its ability to self-learn and adapt asdescribed in FIG. 8, is used to evaluate content for duplication orsimilarity traits.

Perceptual Image Hashes (69) shows a common technique for copyrightdetection known as “Perceptual Image Hashes” is repurposed to helpinterpret the results of the AI Similarity Model (104). The systemtransforms this method known in the art by using the Algorithmic“standard” distance and difference hashes to help interpret learningmodels rather than feeding them. This allows other models, static mediaattributes, and the media data itself to be the primary factors inclassification while still utilizing image hash algorithms to addinsights to the results. This step was a key advancement that led toimproved similarity detection.

Universal Persona (105) shows a Sub-Region Pixel Difference hash that isused to compute if one photo is a subset of another, typically used forcopyright detection, but used here as a part of AI Similarity Model(104) interpretation.

Sub-Region Pixel Distance (106) shows a further extension of methodswell known in the art, here a Sub-Region Pixel Distance hash that isused to compute if one photo is a modified version of another, typicallyused for copyright detection, but used here as a part of AI SimilarityModel (104) interpretation.

In the Personalization Funnel containing the General Content Perspectiveis used to influence and further interpret similarity detection.Personalization is used to help determine if borderline similar contentshould be discarded (107) or demoted (negatively weighted) as in Demotesfor Subjective Similar Feel (70). The Personalization Funnel touchesnearly every process and similarity is no exception. Beyond the typicalsubjective categories, similarity judgements can be influenced bypersonal preferences, normalizing various dimensions of similaritydifferently and effecting natural language generation.

Discards Too Similar (107) shows the system step of Discard Too Similar,which is a key branch in the system processes when content that ispredicted to be too similar to better content is discarded from theoverall set so as to not pollute the results returned to end users withredundant media.

Demotes for Subjective Similar Feel (70) depicts where the systemDemotes for Similar Subjective Feel, content that is not considered aduplicate of better content, but which shares so many similarities thatother content that system users would prefer to see more diverse typesof content ahead of it, are negatively weighted within its dimensions toartificially “demote” it, based upon the individual personalizationpreferences of system users Universal Persona (105).

Promoted One Of A Set (71) depicts where the system promotes a singleitem of a Set of the content that represents the best of a series or“set” of content is promoted through weighting it's coordinates towardsthe meaningfulness center of the hypersphere. This is because the systemuser spent additional time and interest in creating a series andtherefore valued the best of that series/set uniquely.

FIG. 7 is a work flow diagram showing the Realtime Evaluation System. Ina preferred embodiment of the system, Real-time queries use all currentavailable data about end users, their associated users, and all theircurrent content to select meaningful, filtered sets of media on demandfor end users. The method suspends prediction data about media qualitydimensions in a non-aggregated and non-normalized state so it can beused to impact the analysis of content that come after the piece ofcontent in question and most beneficially, to aid in the analysis ofcontent/images that came before the piece of content in question, inreal time, in the analysis queue. This process is not recursive innature as much as it is a suspension of data transformation in anon-normalized state. This data is then used in an ambient analysistechnique across all entities currently being analyzed and waiting inthe queue.

In the preferred embodiment of the system, aggregate and/or real-timephoto evaluation takes a collection of visual content and evaluates ittogether for natural language explanations and comparisons forsimilarity, common faces, etc.

The Real-time Evaluation system starts with Real-Time Queries (72) whereReal-Time Queries act as a trigger to engage a real-time evaluation of aset of content through the system. The key characteristics of thereal-time evaluation is to allow for cross content data to influencesubjective meaningfulness as well as provide a way to influencesubjective meaningfulness as a function of Time (10) by taking intoaccount the timing of the real-time query as well as relevant modelupdates that have occurred through the Personalized ContinuousComposition Learning depicted in FIG. 8. Queries can be triggered fromsimple user requests, such as an unfiltered view of a user's selectedset of content, complex requests involving search criteria or cross-userrequirements, or may be triggered automatically by calendar eventswherein, for example, a user may be prompted to evaluate a content setselected for use in a present for the birthday of a grandparent, e.g. aphotobook created to highlight the games and travel for a child's hockeyteam, etc.

Then in the next step, (120) the Personalization Funnel (03) is onceagain used as a primary input into the interpretation and evaluation offorward models in the system, feeding into Real-Time Queries (72)

Applied Time Distance Weighting (121) depicts how the real-time contentanalysis may consider time-oriented attributes to the content. In thesimplest case it may adjust the weights or filters of content based onquery criteria and the age of given content. In more complex cases itmay model user preferences as a function of data identified over time,such as the frequency of an individual face within a system user's photolibrary, over time.

To further illustrate the invention subsystems working together, the AIPipeline (122) represents aging pre-processed data that was marked forrenewed analysis of static attributes as documented in FIG. 9,represented here as an AI pipeline. At appropriate times, photos thatneed refreshed analysis are run through the new photo journey.

The Non-Normalized N-Dimensional Value Storage (numbered differentlyelsewhere, but here referenced as 123) depicts the data store thatreal-time queries draw upon for the saved data related to individualpieces of content, generated through the process described in FIG. 9.

In the Face ID Weighting step (73), the system face ID weighting ofcontent based on the frequency and timing of subjects found across allrelevant content is used to in the real-time evaluation.

The ability to recognize and demote content that is categorically not agood selection is shown in Meme & Non-Personal Classifier (74). Herecontent that is mixed in with personal photos but represent downloads,“memes”, or other non-personal photos, are recognized and, with a highdegree of certainty, are discarded from further evaluation in ShortCircuit Further Evaluation (124).

To gain further efficiency, in Short Circuit Further Evaluation (124),some content evaluation will result in the ability to skip additionalprocessing, thereby short-circuiting further evaluation. For example, ifevaluating a photo that appears to be a downloaded comic or Internetmeme (74), that photo is tagged as such and additional processing doesnot occur.

It is at this point in real-time evaluation that the invention's novelSimilarity as seen in (13) (14) of or “de-duplication” detectionidentifies what content is too similar to each other to all pass ontogether and which of that content is of the highest quality to promotewhile discarding the rest thereby Short-Circuiting Further Evaluation(124).

At this point in real-time evaluation, the system generated predictiondata begins to transform into analysis outputs regarding the specificmeaningfulness of content to an individual user. In Dimensions, e.g.content attribute evaluations that impact the subjective meaningfulnessof that content, are normalized with respect to each other and throughweights passed down through the personalization funnel (120). Each pieceof content has its own scale based on how independent dimensions couldinfluence others, which dimensions apply to given content and which donot. For example, when evaluating an image, dimensions relating to“people” do not disqualify images which do not have people from beingplotted for subjective meaningfulness, however the lack of people mayscale dimensions in a way to make the “ideal center” or nexus ofmeaningfulness more challenging to achieve for content with anynon-ideal attributes.

In Dimension Normalization (125), plotting content within the normalizedDimension Normalization n-dimensional space along with the idealizedcenter for personalized, subjective meaningfulness is shown. Part of theplotting of these points is returning the distance between the “idealcenter” and the point of the content. If that distance if beyond thelength of a given filter in the real-time query, it can be thought to beoutside the “hypersphere” and may be optionally discarded from furtherevaluation.

The Static Natural Language Generator (76), the Object Natural LanguageGenerator (77), the Grouping Natural Language Generator (78), theSubject Natural Language Generator (79), the Mood Natural LanguageGenerator (80) and the Composition Natural Language Generator (81) alldepict Personalization in Natural Language to explain the systemspredictions, findings, ranking, analysis, etc. in a readable format thatis understandable to the end user. The invention improves on prior artnatural language generation by extending the influence of the subjectiveperspective represented by the user's General Content Perspective (03)into the invention's own natural language generation. That is, alllanguage presented to the user through natural language generation istailored specifically to the individual user's predicted mix of baselinemotivational persona AI models. Each user of the invention willexperience the system's natural language feedback and analysis in aslightly different way matched to the system's unique representation ofthe user's perspectives. The Personalization in Natural Languageinfluences various system NLG actions including but not limited to:

Static Natural Language Generator (76) allows for pre-written, staticlanguage can be triggered to explain common analysis results to endusers.

Object Natural Language Generator (77) uses object detection can addricher descriptions of analysis results to end users.

Grouping Natural Language Generator (78) uses combinations of attributesand attribute conditions to convey a richer description of analysisresults to end users. For example, if a scene in the content is of awedding and the mood is “silly” and the subject is dancing, these threeattributes from three different prior models can be combined into avariety of explicit natural language explaining how the combinationinfluenced the overall plot of the content.

Subject Natural Language Generator (79) allows for generation ofsubjectively meaningful language around analysis of the subject of thecontent.

Mood Natural Language Generator (80) allows for the generation ofsubjectively meaningful language around analysis of the mood of thecontent.

Composition Natural Language Generator (81) allows for the generation ofsubjectively meaningful language around analysis of the composition ofthe content.

The Aesthetics Natural Language Generator (82) allows for the generationof subjectively meaningful language around analysis of the aesthetics ofthe content, often combined with other special attention attributes tocreate a longer, comprehensive sentence about the strongest subjectiveindicators in plotting content. Components of natural language can belook-ups based on hand written content, look-ups based on NLG analysisof similar photos, fully automated generation based on personalization,or a fluid mix of any of these techniques.

Then in Aggregate and Sort (129) real-time query results are aggregatedand sorted according to the criteria of the query across all the contentevaluated in this process.

Content is then transmitted or loaded back to end users in (126) onceanalysis, aggregation, and sorting are complete.

(127) depicts how visual representations of content, originals or copiesof the content, along with statistical and/or language explanationsregarding subjective meaningfulness are made available to the systemuser through visual devices such as phones, tablets, VR/AR headsets,TVs, etc., or in some processes even physical visual mediums such asphotobooks, etc., such that a user (128) benefits from the system.

FIG. 8 is a work flow diagram showing the Personalized ContinuousLearning System component of the invention—a system that uses thesubjective perspective of the individual system user to influence howthe system learns, the strength of what the system learns and thepredictive outputs of future analysis.

FIG. 8 shows how, in a preferred embodiment of the system, thesubjective perspective of the individual system user is used toinfluence how the system learns, the strength of what the system learnsand the predictive outputs of future analysis.

Starting at the User Gallery Feedback Learning Queue (84), the UserSurvey Learning Queue (85) and the User Photo Reinforcement LearningQueue (86) the system is improved over time through explicit andimplicit use of its features, including:

User Gallery Feedback Learning Queue (84) provides the explicit userfeedback elicited as an interaction through a gallery display of contentin which users can like or dislike results and explain their reasoning.This data is stored for periodic system wide updates of model behaviorto continuously improve the system analyses.

The User Survey Learning Queue (85) handles the explicit userpreferences ascertained through visual or verbal surveys. Survey data isstored for periodic system wide updates of model behavior tocontinuously improve the system analyses.

Through User Photo Reinforcement Learning Queue (86), the implicit useof visual content such as ongoing statistical analysis of the volume andattributes of content the user is processing through the system. Thisdata, originating from the Media Flow Through System depicted in FIG. 9,is stored for periodic system wide updates of model behavior tocontinuously improve the system analyses.

Then the Learning Agent (87) depicts how the queue for processinglearning data can be triggered by events that would require theprerequisite training, in the case of processing initial survey data toenhance results for the first real-time query analysis of content asdescribed in FIG. 7. It can also be triggered by other useful states orevents, such as a volume of data threshold or a periodic timer.

The invention uses an independent agent Learning Agent (87), or process,that orchestrates the various learning data gathered from the data pools(131) for updating the personalization funnel (130 in this figure). Inanother implementation, the Learning Agent (87) may update aspects ofmany other relevant models as well. The agent ensures that the rolloutand computation required to update models does not interfere with systemprocessing or performance.

The personalization funnel (130) is then enhanced at each layer throughthe work of the learning agent 87 using the data pooling element (131).In most cases, the Individual Personalization (130 in this figure) isupdated as well as the Persona Personalization models by learning inaggregate from all system users who identify with target personas. TheUniversal Model also learns over time about the aggregate use andfeedback from all system users.

Finally, Future Analysis For Continuous Improvement (88) depicts dataidentified by the learning agent (87) as not suitable or possessingsuspect qualities. In a preferred embodiment of the system, this dataand analysis are saved to the side for manual inspection as the FutureAnalysis for Continuous Improvement (88) report.

FIG. 9 is a work flow diagram illustrating the New Media Flow. As areminder, FIG. 1 is a diagram of the system and its high-level flow fromlarge scale sub-system to sub-system to illustrate how the parts fittogether in the whole. FIG. 9 is an expanded view of the samesub-systems where the focus is shifted from how the sub-systems fittogether, and affect each other, to instead highlight the specificjourney a photo, piece of media or content takes through the preferredembodiment of the invention and the transformations that happen alongthe way as pixel data is transformed into a usable understanding of usersubjective visual perspectives. FIG. 9 depicts the journey of a single,visual content piece as it moves along a journey of AI analysis that iscached and refreshed as the system learns more about the particular enduser.

FIG. 9 depicts how, in a preferred embodiment of the system, contentcreated by a system user flows through various analysis to produceusable data outputs and await final analysis triggers. FIG. 9 shows howthe system uses the visual subjective perspective of the user throughoutthe analysis and how the data produced in the predictive analysis isused for continuous learning and analysis of other content.

The System (300) user starts interacting with the system. The systemuser is a person who has content, media, photos, et al, they havepersonally taken stored on a computer device, however, the “content andmedia users” (1) could be a variety of people, groups or institutions.

The Content Capture (328) Depicts how user content is created throughconsumer content capture devices. The system can also be used with avariety of content created through a wide variety of methods. Visualcontent is not limited to cameras and can be captured from mobiledevices, VR environments, and other consumer electronics.

The Input Plurality Of Content Media element (304) shows the media beingconsumed by the system, either implicitly through automatic fetching oruse of an application specific camera software, or explicitly throughuser selection.

The Static Attribute Processing element (329) depicts how the content'sstatic attributes are processed and prepared into related data packetsto be input into forward models.

The Non-Normalized N-Dimensional Value Storage (103) Shows the mediabeing consumed by the system, either implicitly through automaticfetching or use of an application specific camera software, orexplicitly through user selection.

The Personal and Universal Learning Queues (104) Depicts how thecontent's static attributes are processed and prepared into related datapackets to be input into forward models. These attributes can include,for example, “exif” data such as a photo's date and geolocation tags, aswell as intrinsic properties of the media such as its resolution,duration in the case of video, and any explicit input captured about themedia directly from the end user.

The Violence Model Filtering (301) and the “Adult” Model Filtering (302)depict how media flagged as inappropriate is filtered out early, such asviolent or adult imagery, so as to not waste additional computation aswell as potentially flag the content for administrative review inapplications where moderation is appropriate.

The Whiteout/Blackout item (303) shows how common photo issues, such aseither all white or all black images, are detected and discarded earlyto prevent wasted computation.

The Short Circuit Further Evaluation element (304 in thisfigure)—depicts how many different conditions, such as those in ViolenceModel Filtering (301), the Whiteout/Blackout item (303) the “Adult”Model Filtering (302) the Scene Clarity, Pitch, Yaw, Roll (305) (329),short-circuit additional analysis and are discarded and/or flagged.Efficiency in the form of computational time is saved in the inventionand is an additional improvement over prior art by ejecting media thatdoesn't pass certain large impact criteria for evaluation.

Object and Scene Classifier (306) depicts how some early models used inanalyzing media are object and scene classifiers. This identifiesimportant characteristics about scenes through common labels, such as“#wedding”, as well as individual object labels, such as “#cake”, whichis largely used as forward fed data to other models.

The Aesthetics column (307) shows subjective personalization categories,the individual models comprising these categories reside in thepersonalization funnel of (308 in this figure), broken into commonvisual components that include emotional and aesthetically meaningfulqualities. These subjective categories can be many and varied and arenot limited to the ones described herein. For example, for categorieslike mood there could be subcategory components such as “cheerful” andfor a major category like “aesthetics” there could be a subcategory suchas “good use of color” (or, for negative reactions, corollaries such as“poor use of color”).

The Area and Object Bounds (309) shows how models in the pipeline cansplit and process independent branches, such as in Area and ObjectBounds (309) where area and object bounds are identified to provideforward data into models and relate visual spaces and motion toclassified objects and surfaces.

The Foreground/Background component (310) shows how foreground andbackground detection is used as important data to feed forward to futuremodels, including the percentage of area used for each as well as visualboundaries for foreground and background elements.

Scene Clarity, Pitch, Yaw, Roll (305) contains additional informativealgorithms are then used to analyze media for quality indicators, oftencombined forward in the pipeline to make interpreted determinations,such as the pitch, yaw, or roll detected in the content orientation, aswell as clarity measures which can be further divided by including (310)Foreground and Background Information.

The Personalization Funnel (306 in this figure) which holds the user'sGeneral Content Perspective is applied into the analysis pipeline forsubjective, individualized analysis. This can be looped as needed toinclude multiple individuals in a single analysis for one piece ofcontent.

Then, people (311) that appear within the content can be evaluated forcommon qualifiers that influence subjective evaluations, weighted by thepersonalization funnel (308 in this figure) as well as optionally mergedparallel models as depicted in FIG. 9 with Area and Object Bounds (309),Foreground/Background component (310), and Scene Clarity, Pitch, Yaw,Roll (305).

Face ID (312) At this point in the system flow, facial recognition isused to power additional analytical data for the purpose of determiningsubjective meaningfulness based on the individual connections of anindividual.

Selfie Classifier (313) In a preferred embodiment of the system, a“selfie” feature of the app allows for the identification and attributeclassification of identifying the end user of the system as the subjectof the content.

Action (314) depicts how aspects of motion and action are classified forthe purpose of predicting subjective meaningfulness for an end user.Combined with data from prior models this can create intricatenarratives around “what's happening” in the content, even from stillphotos.

Eyes and Shades (315) shows the analysis of how subject matter eyes,their positions and coverings are presented within the content, forexample, if they are wearing sunglasses.

Then the system provides static elements of similarities attributes. Inother words, it identifies and records hashes and attributes thatcontribute strongly to real time similarity identification andde-duplication, as further described in FIG. 6.

The Composition Depth (317) depicts how composition depth is modeledbased on objective subject matter expert data.

The Depth Interpretation Inner Layer (319) Then, the Composition Depth(317) Composition Depth, is further interpreted as a separate process orinternal layer to The Composition Depth (317) by taking a directapplication of depth indicators such as blur comparing foreground andbackground along with subject size in relation to the framing, etc.

Additional composition models (320) used for the evaluation ofsubjective meaningfulness through the lens of the (308) personalizationfunnel. Some of these models are “symmetric”, meaning they are thesubjective half to objective versions of the composition type.

The Depth Interpretive Inner Layer (318) and the Whole Subject Capture(322) work in a complimentary process, additional objective compositionquality models, trained through analysis of subject matter expert datacontribute to predicting the subjective meaningfulness of a piece ofcontent. Some of these models are “symmetric”, meaning they are theobjective half to subjective versions of the composition type.

Once again, additional composition models (323) are evaluated forsubjective meaningfulness through the lens of the (308) personalizationfunnel. Some of these models can be “asymmetric”, meaning they arecomposition models with no objective mirror type.

Complimentary Color Analysis (321) depicts how complimentary color isevaluated based on the data fed forward from prior models such asforeground/background and surfaces, as well as taking color averagesacross uniquely identifiable areas of the content.

The Waiting Queue (324) shows the use of parallel model data recombinedinto a singular data set for the use of further analysis.

The Non-Normalized N-Dimensional Value Storage (325) depicts how thenon-normalized versions of each static dimension can be stored forfurther analysis through Real-Time Queries (327) or for continuouslearning through Personalization and Universal Learning Queues (326). Itis important to note here that normalization and hypersphere plotting isheld for times when content is evaluated in aggregate in order tocapture aggregate dimensions such as frequency of identifiable people.

Personalization and Universal Learning Queues (326) shows the step whereonce the first pass of static analysis of data for a single piece ofcontent is complete, the Non-Normalized N-Dimensional Value Storage(325) can be leveraged to provide continuous learning and improvementsto the system, as further detailed in FIG. 8. Continuous learning datais queued to be processed in regular batches at computationallyefficient times.

Finally, Real-Time Queries (327) depicts how, when the first pass ofstatic analysis of data for a particular piece of content is complete,the media is available for final analysis triggered by actionsinstigated by the end users or through automated push analysis sent toend users based upon time or event criteria. This process is furtherdetailed in FIG. 7. However, a single piece of media content's journeyis not truly complete until it is also evaluated in aggregate with othercontent, in order to complete analyses for things like similaritiesdetailed in FIG. 6.

FIG. 10 is discussed under the section on FIG. 1 above.

It should be understood that while the preferred embodiments of theinvention are described in some detail herein, the present disclosure ismade by way of example only and that variations and changes thereto arepossible without departing from the subject matter coming within thescope of the following claims, and a reasonable equivalency thereof,which claims I regard as my invention.

All of the material in this patent document is subject to copyrightprotection under the copyright laws of the United States and othercountries. The copyright owner has no objection to the facsimilereproduction by anyone of the patent document or the patent disclosure,as it appears in official governmental records but, otherwise, all othercopyright rights whatsoever are reserved.

REFERENCE NUMBERS USED

-   1. 1. Content and Media Users-   2. Input plurality of content/media.-   3. Personalization Funnel-   4. 4/4A. Universal Model-   5. 5/5A. Persona Model-   6. 6/6A. Individual Model-   7. Implicit Learning-   8. Explicit Learning-   9. Continuous Learning and Improvement-   10. Time Element-   11. Context Element-   12. Static Dimensions-   13. Other Machine Learning Dimensions-   14. Algorithmic Dimensions-   15. Normalize Data-   16. Hypersphere Plot-   17. Idealized, Personal Center-   18. Individual Asset-   19. Remove Assets outside of Hypersphere.-   20. Magnitude Filter-   21. Explanation of dimensions and magnitude relationship natural    language generation.-   22. Presentation of images and explanation of system decisions to    user, content meaningfulness human interactions.-   23. Explicit and Personalization Feedback Loop.-   24. Algorithmic “Difference”-   25. Algorithmic “Distance”-   26. Feature Similarities-   27. Visual Machine Learning Model (CNN).-   28. Positive Short Circuit.-   29. Negative Short Circuit.-   30. Ensemble Learning.-   31. Positive Series.-   32. Negative Series.-   33. Objective Universal Training.-   34. Static Analysis.-   35. Subject Matter Expert Supervised Machine Learning.-   36. Incremental, External Machine Learning.-   37. Quantitative Universal Base.-   38. Subjective Universal Representation-   39. Incremental, Qualitative, Focus Group Machine Learning.-   40. Subjected Persona Representation-   41.Grouped Personal Supervised Machine Learning.-   42. Subjective Individual System User.-   43. Personalization Weights and Biases.-   44. ML Model(s)-   45. Formulas and Static Attributes-   46. Source Code-   47. Dimensions prior to normalization.-   48. Normalized Dimensions.-   49. Media for Analysis-   50. Personalization in Natural Language Generation-   51. Pitch, Roll and Yaw Detection-   52. Focus Detection-   53. Depth Composition Classifier-   54. Depth Composition Generator-   55. Explicit Own Content Evaluation-   56. Symmetry Composition Classifier-   57. Symmetry Composition Generator-   58. Golden Ratio Composition Classifier-   59. Golden Ration Composition Generator-   60. Fill Frame Composition Classifier-   61. Fill Frame Composition Generator-   62. Balance Composition Classifier-   63. Balance Composition Generator-   64. Additional Composition Pair Classifiers-   65. Additional Composition Pair Generator-   66. Pure Subjective Composition Classifiers-   67. Weighted Alternative AI Models-   68. (Blank).-   69. Demotes for Subjective Similar Feel-   70. Promotes 1 of a Set-   71. Real Time Queries-   72. Face ID Weighting-   73. Meme & Non-Personal Classifier-   74. Similarity Interpreter-   75. Static Natural Language Generator-   76. Object Natural Language Generator-   77. Grouping Natural Language Generator-   78. Subject Natural Language Generator-   79. Mood Natural Language Generator-   80. Composition Natural Language Generator-   81. Aesthetics Natural Language Generator-   82. (Blank)-   83. User Gallery Feedback Learning Queue-   84. User Survey Learning Queue-   85. User Photo Reinforcement Learning Queue-   86. Learning Agent-   87. Future Analysis For Continuous Improvement-   100. Mirrored Categories-   101. Multi-Dimensional Weighted Result-   102. Visual Media to Compare-   103. Static Media Attributes-   104. AI Similarity Model-   105. Universal/Persona/Individual Personalization Funnel-   106. Sub-Region Pixel Distance-   107. Discards Too Similar-   120. Universal/Persona/Individual Personalization Funnel-   121. Applied Time Distance Waiting.-   122. AI Pipeline-   123. Non-Normalized N-Dimensional Value Storage-   124. Short Circuit Further Evaluation-   125. Dimension Normalization-   126. Loaded back to End Users-   127. Visual Representations of Content-   128. User-   129. Aggregate & Sort-   130. Universal/Persona/Individual Personalization Funnel-   131. Data Pools: Collection and Forwarding to Learning Agent.-   300. User interface-   301. Violence Model Filtering-   302. “Adult” Model Filtering-   303. Whiteout/Blackout-   304. Short Circuit Further Evaluation.-   305. Scene, Clarity, Pitch, Yaw, Roll element-   306. Aesthetics/Subject/Mood/Composition element.-   307. Object and Scene Classifier-   308. Universal/Persona/Individual Personalization Funnel-   309. Area And Object Bounds-   310. Foreground/Background-   311. People element-   312. Face ID-   313. Selfie Classifier-   314. Action element-   315. Eyes & Shades-   316. Short Circuit/Further Evaluation element-   317. Composition Depth-   318. Depth Interpretation Inner Layer-   319. Symmetry/Golden Ratio/Fill Frame/Balance Objective elements-   320. Symmetry/Golden Ratio/Fill Frame/Balance Subjective elements-   321. Complimentary Color Analysis-   322. Whole Subject Capture element-   323. Subjective Color/Texture/Simplicity Composition-   324. Waiting Queue-   325. Non-Normalized N-Dimensional Value Storage-   326. Personal And Universal Learning Queues-   327. Real-Time Queries.-   328. Blank-   450. Object Detection Model    -   451. Subjection Composition elements    -   452. Objective Composition element-   700. Sub-Region Pixel Difference.-   701. Hypersphere Plotting-   800. General Content Perspective, overall combination.-   801. Individual Content Perspective, overall combination.-   802. Personalization Content Presentation & User Feedback, overall    combination-   803. Hypersphere Analysis, overall element.

That which is claimed:
 1. A system for learning a user perspectiveregarding the meaningfulness of various content from a multitude ofusers, the system comprising: a General Content Perspective (800), anIndividual Content Perspective (801), a Natural Language Generation andContent Presentation (804), and a Hypersphere (803)
 2. The system ofclaim 1, additionally comprising a non-transitory storage device havingembodied therein one or more routines operable to learn a userperspective regarding the meaningfulness of various content from amultitude of users; and one or more processors coupled to thenon-transitory storage device and operable to execute the one or moreroutines.
 3. The system of claim 2, wherein the General ContentPerspective (801) comprises a personalization funnel (3) whichrepresents the General Content Perspective of the user, which whenexecuted by the one or more processors, receives a plurality of contents(2) from a user, and performs explicit and implicit learning byevaluating distribution of characteristics in a received plurality ofcontents (2) and updates personalization funnel models (4, 5, 6)contained in the personalization funnel (3) to incorporate thedistribution of the characteristics in the contents (2) being evaluated,where the General Content Perspective functions to allow the system tolearn and retain one of more perspectives of a user.
 4. The system ofclaim 2, wherein the Individual Content Perspective(802), whichfunctions to produce one or more characteristic predictions about one ormore individual pieces of content, which when executed by the one ormore processors, makes one or more probabilistic predictions about theplurality of contents (2) across a plurality of dimensions to predictone or more dimension values for a user's perspectives regarding theplurality of contents and to remove one or more duplicates and one ormore lesser quality near-duplicates.
 5. The system of claim 2, whereinthe Natural Language Generation and Content Presentation (804) is anexplanatory user interaction process (21, 22), which when executed bythe one or more processors, presents the final set of contents (2) andexplains in natural language a reasoning regarding the meaningfulness ofthe selection of the final set of contents (2), where the NaturalLanguage Generation and Content Presentation allows the system todescribe one or more selections in a personalized natural language thatis customized to an individual system user.
 6. The system of claim 2,wherein the Hypersphere (803) comprises the magnitude filter (19), whichwhen executed by the one or more processors, creates a vector from thehypersphere plot (16) of all the contents (2) to an idealized center ofthe hypersphere plot (16) and measures a magnitude of the vector fromthe idealized center of the hypersphere plot (16) to a boundary of thehypersphere plot (16), so as to remove the contents falling outside themeasured magnitude of the created vector and to select a final set ofcontents (2), where, the hypersphere allows the invention higher levelsof precision and accuracy when analyzing one or more large groups ofcontent.
 7. The system of claim 2, wherein the General ContentPerspective comprises the personalization funnel (3) which representsthe General Content Perspective of the user, which when executed by theone or more processors, receives the plurality of contents (2) from theuser, and performs explicit and implicit learning by evaluatingdistribution of characteristics in the received plurality of contents(2) and updates personalization funnel models (4, 5, 6) contained in thepersonalization funnel (3) to incorporate the distribution of thecharacteristics in the contents (2) being evaluated, where the GeneralContent Perspective functions to allow the system to learn and retainone of more perspectives of the user, wherein the Individual ContentPerspective, which functions to produce one or more content predictionsabout one or more individual pieces of content, which when executed bythe one or more processors, makes one or more probabilistic predictionsabout the plurality of contents (2) across the plurality of dimensionsto predict one or more dimension values for the user's perspectivesregarding the plurality of contents and to remove one or more duplicatesand one or more lesser quality near-duplicates, wherein the NaturalLanguage Generation and Content Presentation the user interaction engine(21, 22), which when executed by the one or more processors, presentsthe final set of contents (2) and explains in natural language thereasoning regarding the meaningfulness of the selection of the final setof contents (2), where the Natural Language Generation and ContentPresentation allows the system to describe one or more selections in thepersonalized natural language that is customized to an individual systemuser, wherein the Hypersphere comprises the magnitude filter (20), whichwhen executed by the one or more processors, creates the vector from thehypersphere plot (16) of all the contents (2) to an idealized center ofthe hypersphere plot (16) and measures the magnitude of the vector fromthe idealized center of the hypersphere plot (16) to the boundary of thehypersphere plot (16), so as to remove the contents falling outside themeasured magnitude of the created vector and to select the final set ofcontents (2), where, the hypersphere allows the invention higher levelsof precision and accuracy when analyzing one or more large groups ofcontent.
 8. The system of claim 7, where the General Content Perspectivecomprises one or more subjective models, where the Individual ContentPerspective comprises one or more objective models, where the systemfirst builds the General Content Perspective and uses the GeneralContent Perspective as a subjective guide to predict one or moreIndividual Content Perspective items, using one or more objective modelsfor each item of media.
 9. The system of claim 7, where the GeneralContent Perspective additionally comprises a subjective compositionelement (451), where the subjective composition element comprises,first, a corresponding set of models within the universal model, second,a corresponding set of models within the one or more persona models, andthird, a corresponding set of models within the individual model, wherethe universal model cross-communicates with a first level of thepersonalization funnel, where the one or more persona modelscross-communicates with a second level of the personalization funnel,and where the individual model cross-communicates with a third, bottomlevel of the personalization funnel.
 10. The system of claim 9, wherethe General Content Perspective additionally comprises an ExplicitLearning element (8), a Continuous Learning and Improvement element (9),a Time element (10), and a Context element (11), where thepersonalization funnel performs an implicit learning function (7). 11.The system of claim 7, where the Individual Content Perspective elementcomprises one or more static dimensions (12), one or more subjectivedimensions, at least one other machine learning dimension (13), and oneor more algorithmic dimensions (14).
 12. The system of claim 7, wherethe Hypersphere additionally comprises a normalize data element.
 13. Thesystem of claim 12, where the Hypersphere interacts with the NaturalLanguage Generation and Content Presentation element.
 14. The system ofclaim 12, where the Hypersphere additionally receives data from theIndividual Content Perspective model system which comprises an ML Model(44), which transmits a first data to a Machine Learning Dimensionselement (13), a Formulas and Static Attributes element (45), whichtransmits a second data to a Static Dimensions element (12), and aSource Code element (46), which transmits a third data to an AlgorithmicDimensions element (14), where the first data, the second data, and thethird data and transmitted to a Dimensions Prior to Normalizationelement (47).
 15. The system of claim 14, where the data received fromthe Normalization Engine (47) into Hypersphere additionally comprises anEnsemble Machine Learning Dimensions element (13), which acquires afourth data from the Machine Learning Dimensions element (13), theStatic Dimensions Element (12) and the Algorithmic Dimensions element(14), where the fourth data is transmitted to the Dimensions Prior toNormalization element (47).
 16. The system of claim 15, where theDimensions Prior to Normalization element (47) transmits a fifth data toa Personalization Weighted Normalization element (15), which, in turn,produces a Normalized Dimensions element (48) which is transmitted tothe Hypersphere Plot (16), where an Idealized Personal Center/Nexus ofMeaningfulness element (17) and an Individual Asset Inside Hypersphereelement (18) interact with the system.
 17. The system of claim 7, wherethe Natural Language Generation and Content Presentation (804) elementinteracts with the Hypersphere (803) and the General Content Perspective(801), where the Natural Language Generation and Content Presentation(804) element interacts with the General Content Perspective (801) for alanguage and content presentation personalization element, where theNatural Language Generation and Content Presentation element involves astep of presenting a selected set of images to a user for contentmeaningfulness through human interactions, where the step of presentinga selected set of images to a user for content meaningfulness throughhuman interactions includes an Explain Dimensions & MagnitudeRelationships Natural Language Generation step, and a Content Ordered byMagnitude of Vector to Ideal Center step.
 18. A method for learning auser perspective regarding the meaningfulness of various content from amultitude of people, the method comprising: utilizing a systemcomprising the General Content Perspective (800), the Individual ContentPerspective (801), the Natural Language Generation and ContentPresentation (804), and the Hypersphere (803), then using that system toprovide a general content perception and selection system that includesat least one feedback element and at least one learning element, where,the system, allows for an ongoing improvement in the system'sunderstanding of the user perspective using the user's own content as aguide at the beginning of the process by modeling what the systeminitially believes are the best and most predictive subjective visualperspectives of an individual before attempting to evaluate the contentitself.
 19. The method of claim 18, additionally comprising a secondstep, where the plurality of contents (2) are first received at thepersonalization funnel (3) from the user for performing implicitlearning by evaluating distribution of characteristics in the receivedplurality of contents (2), updating personalization funnel models (4, 5,6) contained in the personalization funnel (3) to incorporate thedistribution of the characteristics in the contents (2) being evaluated.20. The method of claim 19, wherein the system additionally comprises ade-duplication process to determine contents which are identical andnear duplicate, including a removal step of removing the contents whichare identical and near duplicate, thereby selecting a preferred set offor presentation to the user as the final set of contents (2).